COURSE UNIT TITLE

: INTRODUCTION TO PATTERN RECOGNITION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CME 4410 INTRODUCTION TO PATTERN RECOGNITION ELECTIVE 2 2 0 6

Offered By

Computer Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

INSTRUCTOR ÖZLEM ÖZTÜRK

Offered to

Computer Engineering

Course Objective

The aim of this course is to learn a computer (by examples) to recognize patterns in noisy data sets (e.g. input-output relations).

Learning Outcomes of the Course Unit

1   Understand high level statistical information
2   Identify where, when and how pattern recognition can be applied
3   Estimate parameters using statistical methods
4   Analyze real world data by means of modelling
5   Design systems for data analysis in multidiciplinary projects

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Pattern Recognition
2 Review of Probability Theorem
3 Bayes Decision Theory
4 Normal Density and Discriminant Functions
5 Maximum Likelihood and Bayesian Parameter Estimation
6 Fisher's Linear Discriminant and Expectation Maximization
7 Non-Parametric Approaches
8 Problem Solving, MIDTERM
9 Distance Based Methods - Nearest Neighborhood Classification
10 Linear Discriminant Functions
11 Unsupervised Learning
12 Clustering
13 Student Presentations
14 Problem Solving

Recomended or Required Reading

Main Book: Duda R. O., Hart P. E., Stork D. G., (2001), Pattern Classification, John Wiley and Sons.
Supplementary Book: C. M. Bishop, (2006), Pattern Recognition and Machine Learning, Springer.

Planned Learning Activities and Teaching Methods

Lecturing, Problem solving, Presentation, Term project, Homework and Laboratory applications

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 LAB LABORATORY
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + LAB * 0.20 + FIN * 0.50
5 RST RESIT
6 FCG FINAL COURSE GRADE MTE * 0.30 + LAB * 0.20 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Statistical knowledge will be evaluated by means of midterm and final exams.
Laboratory applications will involve sample problems that require parameter estimation using statistical methods.
Within the scope of term project, students will analyze real world data using pattern recognition methods.
Pattern recognition problems are usually encountered in multidiciplinary projects. Term projects will provide students to study as part of multidiciplinary projects.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Özlem Öztürk
Bilgisayar Mühendisliği Bölümü
Dokuz Eylül Üniversitesi
Tınaztepe Kampüsü,
Kaynaklar-Buca
Izmir
ozlem.ozturk@cs.deu.edu.tr
+90 232 3017417

Office Hours

Tue: 13:00-14:30
Wed: 13:00-14:30
Fri: 08:30-10:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparation for final exam 1 6 6
Preparation for midterm exam 1 4 4
Preparing assignments 5 4 20
Preparing presentations 1 22 22
Preparations before/after weekly lectures 14 2 28
Preparation for final exam 0 6 0
Preparation for midterm exam 0 4 0
Midterm 1 1 1
Final 1 2 2
TOTAL WORKLOAD (hours) 139

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.154
LO.254
LO.3544
LO.44444
LO.5

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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